Stochastic Bio-Inspired Gene Optimization Based Trapezoidal Fuzzy Logic for Software Failure Prediction Based On Event Log Files - Saravanan, P; Sangeetha, V (International Journal of Computer Sciences and Engineering, 2019)
Software fault detection plays a significant role in the management of software systems quality to locate the fault and to identify the cause. Few research works has been developed for detecting the cause of failure occurrence from event log files. Performance of conventional software failure prediction technique was not effective. In order to overcome such limitation, a Stochastic Bio-inspired Genetic-based Trapezoidal Fuzzy Logic (SBG-TFL) Model is proposed using event log files. The SBG-TFL Model is designed to identify the failure cause with the portfolio formation of good parameters. The SBGTFL Model at first constructs the projects portfolio with help of optimal parameters selected from event log files with application of Stochastic Bio-inspired Gene Optimization (SBGO) Algorithm. The formation of projects portfolio assists for SBG-TFL Model to reduce the amount of time taken for analysing the failure behaviour of a systems application. SBG-TFL Model applies Trapezoidal Fuzzy Logic Model to formulated projects portfolio in order to effectively predict the failure causes of software application. SBG-TFL Model increases the accuracy and true positive rate of software failure prediction. The SBGTFL Model conducts the experimental process on metrics such as recall precision and software failure identification time with respect to different software code size. The experimental result shows that SBG-TFL Model is able to improve the precision of software failure detection and also reduces software failure identification time when compared to state-of-the-art-works.